Blockchain applications often rely on lightweight clients to access and verify on-chain data efficiently without the need to run a resource-intensive full node. These light clients must maintain robust security to protect the blockchain's integrity for users of applications built upon it, achieving this with minimal resources and without significant latency. Moreover, different applications have varying security needs. This work focuses on addressing these two key requirements in the context of Proof-of-Stake (PoS) blockchains and identifying the fundamental cost-latency trade-offs to achieve tailored, optimal security for each light client. The key security guarantee of PoS blockchains is economic (implied by the "stake"). In this paper we formalize this cryptoeconomic security to light clients, ensuring that the cost of corrupting the data provided to light clients must outweigh the potential profit, thereby economically deterring malicious actors. We further introduce "insured" cryptoeconomic security to light clients, providing unconditional protection via the attribution of adversarial actions and the consequent slashing of stakes. The divisible and fungible nature of stake facilitates programmable security, allowing for customization of the security level and insurance amount according to the specific needs of different applications. We implemented the protocols in less than 1000 lines of Solidity and TypeScript code and evaluated their gas cost, latency, and the computational overhead. For example, for a transaction with value of \$32k, the light client can choose between zero cost with a latency of 5 hours or instant confirmation with an insurance cost of \$7.45. Thus, the client can select the optimal point on the latency-cost trade-off spectrum that best aligns with its needs. Light clients require negligible storage and face minimal computational costs,...
Intelligent management of power flow and storage balance has proven its worth in supporting economic, sustainable operation of microgrids powered mainly by intermittent renewable energy resources. In particular, the introduction of Multi-Agent Reinforcement Learning (MARL) to solve power management and storage balance problems has been very successful. MARL primary-secondary control was the subject of a recent application in solving power storage flow problems in battery-based micro- and smart-grids, focusing upon vehicle-to-grid applications under realistic environmental considerations such as infrastructural influences. Such influences can worsen the accuracy of plug-and-play batteries' charge-discharge synchronization and hence control stabilization, power flow balance, batteries' health/life, and energy efficiency. This paper proposes a solution to this issue in a DC autonomous microgrid with multiple, heterogeneous batteries. Multiagent-neighbor-to-neighbor information is exploited to enhance the real-time balance of the load participation, and a real-time decentralized infrastructure compensation and power flow organization consumption/loss is established to compensate for infrastructural/environmental influence on the control. Moreover, implementation in a real-time economic sustainable participation policy on each BESS in a test microgrid is explored. The results verify improved synchronization of the batteriesâ power flow with reduced plug-and-play time by (4.16%), enhanced output voltage balance by (2.76-8%), reduced power consumption by (1.908-2.94%), improved power flow balance by (2.765-6.486%), and better power flow efficiency by (0.9196-2.626%) when compared to a baseline MARL implementation.
In this chapter we discuss pseudorandom generators. Loosely speaking, these are efficient deterministic programs that expand short, randomly selected seeds into much longer âpseudorandomâ bit sequences (see illustration in Figure 3.1). Pseudorandom sequences are defined as computationally indistinguishable from truly random sequences by efficient algorithms. Hence the notion of computational indistinguishability (i.e., indistinguishability by efficient procedures) plays a pivotal role in our discussion. Furthermore, the notion of computational indistinguishability plays a key role also in subsequent chapters, in particular in the discussions of secure encryption, zero-knowledge proofs, and cryptographic protocols. The theory of pseudorandomness is also applied to functions, resulting in the notion of pseudorandom functions, which is a useful tool for many cryptographic applications. In addition to definitions of pseudorandom distributions, pseudorandom generators, and pseudorandom functions, this chapter contains constructions of pseudorandom generators (and pseudorandom functions) based on various types of one-way functions. In particular, very simple and efficient pseudorandom generators are constructed based on the existence of one-way permutations. We highlight the hybrid technique , which plays a central role in many of the proofs. (For the first use and further discussion of this technique, see Section 3.2.3.) Organization . Basic discussions, definitions, and constructions of pseudorandom generators appear in Sections 3.1â3.4: We start with a motivating discussion (Section 3.1), proceed with a general definition of computational indistinguishability (Section 3.2) next present and discuss definitions of pseudorandom generators (Section 3.3), and finally present some simple constructions (Section 3.4). More general constructions are discussed in Section 3.5.